WO2019127744A1 - Procédé et classificateur de modélisation automatique d'un modèle de données d'olap - Google Patents

Procédé et classificateur de modélisation automatique d'un modèle de données d'olap Download PDF

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WO2019127744A1
WO2019127744A1 PCT/CN2018/073320 CN2018073320W WO2019127744A1 WO 2019127744 A1 WO2019127744 A1 WO 2019127744A1 CN 2018073320 W CN2018073320 W CN 2018073320W WO 2019127744 A1 WO2019127744 A1 WO 2019127744A1
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query
column
modes
rule
difference degree
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PCT/CN2018/073320
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Chinese (zh)
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李栋
张逸凡
李扬
韩卿
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上海跬智信息技术有限公司
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Priority to US15/769,397 priority Critical patent/US11055307B2/en
Priority to EP18894747.7A priority patent/EP3709192A4/fr
Publication of WO2019127744A1 publication Critical patent/WO2019127744A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/211—Schema design and management
    • G06F16/212—Schema design and management with details for data modelling support
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G06F16/254—Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2282—Tablespace storage structures; Management thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2455—Query execution
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2465—Query processing support for facilitating data mining operations in structured databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G06F16/258—Data format conversion from or to a database
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G06F16/285—Clustering or classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models

Definitions

  • the invention belongs to the field of OLAP big data information, and particularly relates to a method and a classifier for automatic modeling of an OLAP data model.
  • the underlying architecture of OLAP analysis is a data warehouse, which contains a series of data tables.
  • Modelers design data models based on these tables based on business analysis requirements for analysts to use; ultimately, analysts' analysis operations are transformed into a series of SQL query for the data table.
  • the data model gives the data table business meaning, decoupling the relationship between the underlying data and business needs, is an integral part of the entire architecture.
  • the data scale can reach 100 billion or trillion, and the number of dimensions is too large, the business scenario is complex and changeable, which increases the difficulty of data modeling; the use of OLAP Cube does not recommend frequent data models. The changes have increased the difficulty of trial and error in data modeling. These all pose great challenges for the modelers. How to implement automatic modeling through computer algorithms to reduce the cost of manual modeling is particularly important.
  • the technical problem to be solved by the present invention is that the existing OLAP model is extremely dependent on the artificial understanding of the data table and the business requirements, and the modeling efficiency is low, the cost is large, and automatic modeling cannot be realized.
  • the present invention provides a method for automatically modeling an OLAP data model, the method comprising:
  • the beneficial effects of the present invention through the above method, parsing and extracting features of the input SQL sample, and finding patterns required by the data model; then clustering and merging these patterns to generate all required data models,
  • the generated data model fully supports all input SQL and has certain generalization capabilities.
  • the generated data model can fully support the Cube operation and fast query, and ensure that the Cube expansion rate is within 10 times, and the automatic modeling effectively reduces the user's learning difficulty and trial and error costs, and optimizes the user experience.
  • the column information refers to all columns used in the SQL query statement, wherein each column includes: a column name of the column, a table in which the column is located, a type of the column, and a number of times the column appears.
  • the table information refers to all the tables used in the SQL query statement, wherein each table includes: a table name of the table, a type of the table, and associated information of the table and the fact table.
  • classifying the at least N groups of query modes in the S4 includes:
  • S41 Perform a difference judgment on the at least N groups of query modes by using a static rule, where the static rule refers to calculating a difference degree of the same table in any two sets of query modes, and determining whether the difference degree of the table is greater than the first The preset threshold, if greater, the two sets of query modes cannot be classified into one category;
  • the difference degree of the table After the difference degree of the table is calculated, the difference degree of the same column in any two sets of query modes is calculated, and it is determined whether the difference degree of the column is greater than the first preset threshold. one type;
  • the at least N groups of query modes are clustered by using a statistical rule and a preset learning rule.
  • the clustering of the at least N groups of query modes by using the statistical rule and the preset learning rule in the S42 includes:
  • S421 Calculate a feature vector of each group of query modes according to column information and table information used in each group of query modes;
  • the preset learning rule refers to determining whether the clustering result meets a preset criterion after clustering. If not, the supervised machine learning algorithm is used to adjust the clustering result, and the adjusted aggregate is recorded. Class result.
  • converting the mode relationship tree into a corresponding data model in the S6 includes:
  • the column information in the pattern relationship tree is converted into column information of the corresponding data model.
  • the converting the column information in the mode relationship tree into the column information of the corresponding data model further includes:
  • PartScore(i) PartFunc(Score(i), Stats(i)), where PartFunc() is the scoring function, Score(i) is the score in column (i) of each set of query patterns, and Stats(i) is the first ( i) The characteristic statistics of the column, when the (i)th column partition score PartScore(i) exceeds the predetermined scoring threshold, the (i)th column is set as the partition column.
  • the invention also relates to a classifier, comprising: a static rule classification module, a statistical rule and a preset learning rule classification module;
  • the static rule classification module is configured to perform a difference judgment on at least N groups of query modes by using a static rule, where the static rule refers to calculating a difference degree of the same table in any two sets of query modes, and determining a difference of the table. Whether the degree is greater than the first preset threshold, if greater than, the two sets of query modes cannot be classified into one category, where N is a natural number greater than or equal to 1;
  • the difference degree of the table After the difference degree of the table is calculated, the difference degree of the same column in any two sets of query modes is calculated, and it is determined whether the difference degree of the column is greater than the first preset threshold. one type;
  • the statistical rule and the preset learning rule classification module are configured to cluster the at least N groups of query modes by using a statistical rule and a preset learning rule after performing the difference judgment.
  • the preset learning rule refers to determining whether the clustering result meets a preset criterion after clustering. If not, the supervised machine learning algorithm is used to adjust the clustering result, and the adjusted aggregate is recorded. Class result.
  • FIG. 1 is a flow chart of a method for automatically modeling an OLAP data model according to the present invention
  • FIG. 2 is a schematic structural view of a classifier of the present invention.
  • a method for automatically modeling an OLAP data model comprising:
  • step S1 is to perform pre-checking on the obtained SQL query statement, so that it can test whether each input SQL query statement can effectively function in a subsequent stage
  • step S2 adopts SQL syntax.
  • the parser parses the input SQL query statement to determine whether there is a lexical or grammatical error in each SQL query statement; if it exists, it gives an explicit error prompt to guide the user to correct the SQL query statement, if all the verification passes, It is allowed to proceed to the next step S3.
  • step S3 the input SQL query statement is simulated.
  • the engine does not return any meaningful query results. Instead, the analysis results of the query plan are collected and converted into a set of query modes during execution.
  • a lot of SQL will contain: multiple sub-queries (such as SQL containing sub-queries, each sub-query and main query corresponds to a context (ie query structure)), each context will generate a query mode; therefore a SQL may generate multiple query patterns. For example: If the input SQL query statement is:
  • Col(0) sellers.seller_name-name(0): seller_name, tbl(0): sellers, Cat(0): dimension, Score(0): calculated based on the number of occurrences 1, using by group.
  • Col(1) order.location-name(1): location
  • tbl(1) order
  • Cat(1) dimension
  • Score(1) calculated based on the number of occurrences 1, using by group.
  • Col(2) order.price-name(2): price
  • tbl(2) order
  • Cat(2) metric
  • Score(2) calculated based on the number of occurrences 1, usage (metric).
  • Col(3) order.time-name(3): time
  • tbl(3) order
  • Cat(3) dimension
  • Score(3) calculated based on the number of occurrences 1, the use of the filter.
  • the query structure is extracted from the SQL query statement to obtain at least N sets of query modes.
  • step S4 at least N sets of query modes are classified
  • Col(0) sellers.seller_name-name(0): seller_name, tbl(0): sellers, Cat(0): dimension, Score(0): calculated based on the number of occurrences 1, using by group.
  • Col(1) order.location-name(1): location
  • tbl(1) order
  • Cat(1) dimension
  • Score(1) calculated based on the number of occurrences 1, using by group.
  • Col(2) order.price-name(2): price
  • tbl(2) order
  • Cat(2) metric
  • Score(2) calculated based on the number of occurrences 1, usage (metric).
  • Col(0) order.time-name(0): time
  • tbl(0) order
  • Cat(0) dimension
  • Score(0) based on the number of occurrences 1, the use of the filter (filter);
  • Tbl(0) order-Name(0): order, Cat(0): fact table;
  • step S5 the query patterns in each class are merged to obtain a corresponding pattern relationship tree, and the following merge process is to take a union:
  • Col(0) sellers.seller_name-name(0): seller_name, tbl(0): sellers, Cat(0): dimension, Score(0): based on the number of occurrences 1, the use of (group by);
  • Col(1) order.location-name(1):location,tbl(1):order,Cat(1): dimension, Score(1): based on the number of occurrences 1, the use of (group by);
  • Col(2) order.price-name(2): price
  • tbl(2) order
  • Cat(2) metric
  • Score(2) calculated based on the number of occurrences 1, usage (metric);
  • Col(3) order.time-name(3):time,tbl(3):order,Cat(3): dimension, Score(3): based on the number of occurrences 1, the use of the filter (filter);
  • Col(4) buyers.buyer_name-name(4): buyer_name, tbl(4): buyers, Cat(4): dimension, Score(4): based on the number of occurrences 1, use by (group by);
  • step S6 the query mode is classified in the previous step in step S6, and a pattern relationship tree is generated. Each pattern relationship tree will generate a data model at this step.
  • the process from the schema relation tree to the data model is mainly data conversion, that is, the table information in the pattern relationship tree (such as fact table, dimension table, JOIN, etc.) is directly converted into table information in the data model; similarly, the pattern relationship tree Column information (dimensions, measures, computed columns, and so on) is directly translated into column information in the data model. So after a successful set of data models is created, this set of data models can collectively support the input set of SQL queries and the business analysis needs behind those SQLs.
  • the table information in the pattern relationship tree such as fact table, dimension table, JOIN, etc.
  • Column information dimensions, measures, computed columns, and so on
  • the column information refers to all columns used in the SQL query statement, wherein each column includes: a column name of the column, a table in which the column is located, and a type of the column. And the number of times the column appears.
  • the table information refers to all the tables used in the SQL query statement, wherein each table includes: a table name of the table, a type of the table, and associated information of the table and the fact table.
  • Col(i) column information of all the columns used in the SQL query statement in the second embodiment is denoted as Col(i), and includes all the columns used in the context.
  • Name(i) represents the column name of this column
  • Tab(i) represents the table in which the column is located
  • Cat(i) represents the type of the column, such as dimensions, metrics, computed columns, etc.
  • Score( i) represents a score calculated based on the number of occurrences, usage, etc. in this column.
  • Table information Recorded as Tab(j), containing all the tables used in the context.
  • Name(j) represents the table name of the table
  • Cat(j) represents the type of the table, such as fact table, dimension table, etc.
  • Join(j) represents the JOIN association of this table and the fact table.
  • Information including JOIN type, JOIN condition.
  • classifying the at least N groups of query modes in S4 in another embodiment 3 includes:
  • S41 Perform a difference judgment on the at least N groups of query modes by using a static rule, where the static rule refers to calculating a difference degree of the same table in any two sets of query modes, and determining whether the difference degree of the table is greater than the first The preset threshold, if greater, the two sets of query modes cannot be classified into one category;
  • the difference degree of the table After the difference degree of the table is calculated, the difference degree of the same column in any two sets of query modes is calculated, and it is determined whether the difference degree of the column is greater than the first preset threshold. one type;
  • the at least N groups of query modes are clustered by using a statistical rule and a preset learning rule.
  • the general query mode classification includes a plurality of rules, wherein the static rules include a series of mutually exclusive rules, that is, two modes that meet certain conditions cannot be classified into one class. mainly includes:
  • the difference degree Diff(i,j) is calculated for the same column in the two query modes. If the degree of difference of the column is greater than the set threshold, the two query modes cannot be classified into one class.
  • the at least N groups of query modes are clustered by using a statistical rule and a preset learning rule.
  • clustering the at least N groups of query modes by using a statistical rule and a preset learning rule in the S42 in another embodiment 4 includes:
  • S421 Calculate a feature vector of each group of query modes according to column information and table information used in each group of query modes;
  • the feature vector is calculated based on all the column information and the table information of the query mode, for example:
  • X(i) (col1, col2, col3, ...colN, tab1, tab2,...,tabM), where each colX represents the score Score(X) of the Col(X) column, and each tabY represents the Tab(Y) table. Score Score(Y).
  • the preset learning rule refers to determining whether the clustering result meets a preset criterion after clustering, and if not, using a supervised machine learning algorithm to perform clustering results. Adjust and record the adjusted cluster results.
  • the learning rule “static rule” and “statistical rule” generate a recommendation result, and the user can manually adjust or correct the classification result; for example, some query modes are “ After the static rule and the "statistical rule” are judged, they can be classified into one category, but the user manually modifies from the perspective of business and the like to be inseparable.
  • the “learning rules” will save all the final classification results after the user has confirmed, and use the supervised machine learning to train the appropriate classification model to predict whether a group of patterns can be classified into one category, and based on this prediction, the clustering results. Make adjustments.
  • the query mode in each class is merged in the S5, and the corresponding mode relationship tree is obtained:
  • the structure formed by the merged query pattern of each class contains a set of tables and their JOIN relationship trees, and also defines the selected columns on each table.
  • Such a structure is called a schema relation tree.
  • converting the mode relationship tree into a corresponding data model in the S6 in another embodiment 7 includes:
  • the column information in the pattern relationship tree is converted into column information of the corresponding data model.
  • converting the column information in the mode relationship tree into the column information of the corresponding data model in another embodiment 8 further includes:
  • PartScore(i) PartFunc(Score(i), Stats(i)), where PartFunc() is the scoring function, Score(i) is the score in column (i) of each set of query patterns, and Stats(i) is the first ( i) The characteristic statistics of the column (ie, the statistical value of the data feature), when the (i)th column partition score PartScore(i) exceeds the predetermined score threshold, the (i)th column is set as the partition column.
  • Embodiment 9 of the present invention further relates to a classifier, including: a static rule classification module, a statistical rule, and a preset learning rule classification module;
  • the static rule classification module is configured to perform a difference judgment on at least N groups of query modes by using a static rule, where the static rule refers to calculating a difference degree of the same table in any two sets of query modes, and determining a difference of the table. Whether the degree is greater than the first preset threshold, if greater than, the two groups of query modes cannot be classified into one category;
  • the difference degree of the table After the difference degree of the table is calculated, the difference degree of the same column in any two sets of query modes is calculated, and it is determined whether the difference degree of the column is greater than the first preset threshold. one type;
  • the statistical rule and the preset learning rule classification module are configured to cluster the at least N groups of query modes by using a statistical rule and a preset learning rule after performing the difference judgment.
  • the classifier is used to classify multiple query modes, and the query mode classification includes three rules: a static rule: a series of mutually exclusive rules, that is, two modes that meet certain conditions. Can not be divided into one category.
  • the method mainly includes: calculating a difference degree Diff(i, j) for the same table in the two modes, and if the degree of difference is greater than a set threshold, the two modes are not classified into one class;
  • the difference degree Diff(i,j) is calculated for the same column in the two query modes, and if the degree of difference is greater than the set threshold, the two query modes cannot be classified into one class;
  • the merge rules are:
  • each type of query pattern is merged to form a structure containing a set of tables and their JOIN relationship tree, and also defines the selected column on each table.
  • a structure is called a pattern relationship tree.

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Abstract

La présente invention concerne un procédé et un classificateur destinés à la modélisation automatique d'un modèle de données de traitement analytique en ligne (OLAP), le procédé comprenant les étapes consistant : à obtenir une instruction d'interrogation de langage structuré d'interrogation (SQL) ; à analyser l'instruction d'interrogation de SQL pour déterminer si une grammaire ou une erreur lexicale est présente dans l'instruction d'interrogation de SQL ; s'il n'y en a pas, à réaliser une extraction de structure d'interrogation sur l'instruction d'interrogation de SQL pour obtenir au moins N ensembles de modes d'interrogation ; à classifier lesdits N ensembles de modes d'interrogation ; à combiner les modes d'interrogation dans chaque classe pour obtenir un arbre de relation de mode correspondant ; et à convertir l'arbre de relation de mode selon un modèle de données correspondant. La présente invention concerne également un classificateur, le classificateur comprenant : un module de classification de règle statique ainsi qu'une règle statistique et un module préétabli de classification de règle d'apprentissage. Le modèle de données généré au moyen de la présente invention peut entièrement prendre en charge la totalité des SQL d'entrée, tout en présentant certaines capacités de généralisation, et peut prendre entièrement en charge une opération de cube et une interrogation rapide, ce qui permet de réduire efficacement la difficulté d'apprentissage et les coûts liés aux erreurs d'essai pour un utilisateur.
PCT/CN2018/073320 2017-12-29 2018-01-19 Procédé et classificateur de modélisation automatique d'un modèle de données d'olap WO2019127744A1 (fr)

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